使用一组观察序列进行 Scikit Learn HMM 训练 [英] Scikit Learn HMM training with set of observation sequences

问题描述

我有一个问题，关于如何使用 scikit-learn 包中的 gaussianHMM 同时训练多个不同的观察序列.示例如下:可视化股票市场结构

I had a question about how I can use gaussianHMM in the scikit-learn package to train on several different observation sequences all at once. The example is here: visualizing the stock market structure

显示 EM 收敛于 1 个长观察序列.但是在许多情况下，我们希望将每个观察序列分解为具有 START 和 END 状态的观察(例如对句子集的训练).也就是说，我想对多个观察序列进行全局训练.使用 GuassianHMM 时如何实现这一目标?有例子可以看吗?

shows EM converging on 1 long observation sequence. But in many scenarios, we want to break up the observations (like training on set of sentences) with each observation sequence having a START and END state. That is, I would like to globally train on multiple observation sequences. How can one accomplish this when using GuassianHMM? Is there a example to look at?

提前致谢

推荐答案

在附加的例子中你做

model.fit([X])

这是对单个观察的训练，如果您有多个观察，例如 X1、X2、X3，您可以运行

which is training on a singleton of observations, if you have multiple ones, for example X1,X2,X3 you can run

model.fit([X1,X2,X3])

一般来说，对于 scikit-learn 中的 HMM 实现，你给它一个观察序列

in general for HMM implementation in scikit-learn you give it a sequence of observations S

model.fit(S)

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